Matching a human response versus authorizing an effect: ARQUIA-EVAL-001

[argensite]

Correlating a response with the right waiting workflow is different from deciding whether an effect is still authorized. An event can belong to the expected user and wait operation, yet arrive after the permission relevant to that operation has been withdrawn. Durable context should preserve both the response and the revocation.

The LlamaIndex human-in-the-loop guide demonstrates request/response events, matching requirements and saving context for a later continuation. A proposed application test is to record an approval request for one concrete action, persist the context, revoke the grant, and then deliver the matching response. A useful report separates successful event correlation from a fresh authorization check at the tool boundary. It should also distinguish “not executed” from an effect that happened but whose receipt arrived late. This is a test idea, not a reported LlamaIndex bug.

ARQUIA-EVAL-001 v0.1.0 supplies a narrow, framework-independent starting fixture: 28 synthetic cases, five deliberately faulty controls, full traces and an interface for alternative implementations. We invite independent agent and workflow developers to reproduce it, expose missing assumptions or implement a separate candidate. The release does not integrate with LlamaIndex or measure its event system.

Inspect the frozen release, then run locally with Python 3.10+ and the standard library:

python3 verify_package.py
python3 runner.py --evaluator YOUR_NAME > independent_result.json
python3 -m unittest -v test_contract
python3 runner.py --negative-control receipt-renewal > negative_result.json

No network, API keys, model or payment is needed. The reference is expected to exit 0; that deliberate fault should exit 1. Preserve complete output and exit codes, the environment and the exact version; publish the report somewhere you control.

ARQUÍA is José María Garbini's research proposal connecting governance with preservation of relevant relationships and responsibilities. Its engineering bridge, DOI 10.5281/zenodo.21791061, motivates keeping evidence, authority and effects distinct. The open call and four-source DOI map explain the wider proposal and how to submit criticism.

This invitation was prepared by an AI assistant on behalf of the author. The fixture's observer and ordering are simulated; JSON restoration does not establish real distributed recovery. Included passes are author-side checks, not independent validation. Negative results and criticism without execution are welcome.